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Quality-aware data abstraction layer for collaborative 2-tier sensor network applications. (English) Zbl 1243.68060

Summary: This paper presents PRIDE, a novel data abstraction layer for collaborative 2-tier sensor network applications. PRIDE, more specifically, targets distributed real-time applications, in which multiple collaborative mobile devices have to analyze a global situation by collecting and managing data streams from massive underlying sensors. PRIDE at these devices hides the details of underlying sensors and provides transparent, timely, and robust access to global sensor data under highly dynamic and unpredictable environments of emerging sensor network applications. For transparent and efficient sharing of global sensor data, a model-based predictive replication mechanism is proposed and integrated into a conventional data management system that supports diverse types of spatial and temporal queries. In addition, for robust and timely query processing, the predictive replication scheme is extended to the problem of guaranteeing Quality-of-Service (QoS) by introducing feedback control of the accuracy bounds of models. We show the viability of the proposed solution by implementing and evaluating it on a 2-tier sensor network testbed, emulating collaborative search-and-rescue tasks with realistic workloads. Our evaluation results demonstrate that PRIDE can achieve timely sensor data sharing among a large number of devices in a highly robust and controlled manner.

MSC:

68M14 Distributed systems
68M10 Network design and communication in computer systems
93B52 Feedback control

Software:

MauveDB; Oracle
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References:

[1] Communication and networking technologies for public safety (2008) National Institute of Standards and Technology. http://w3.antd.nist.gov/comm_net_ps.shtml
[2] Fire growth and smoke transport modeling with CFAST (2008). http://fast.nist.gov/
[3] Fire information and rescue equipment (FIRE) project (2008). http://fire.me.berkeley.edu/
[4] Nokia N-series (2008). http://www.nseries.com/
[5] Oracle Berkeley DB (2008). http://www.oracle.com
[6] IEEE portable applications (2009). http://standards.ieee.org/regauth/posix
[7] Abbasi AA, Younis M (2007) A survey on clustering algorithms for wireless sensor networks. Comput Commun 30:2826–2841 · Zbl 05398019 · doi:10.1016/j.comcom.2007.05.024
[8] Abdelzaher T, Blum B, Cao Q, Chen Y, Evans D, George J, George S, Gu L, He T, Krishnamurthy S, Luo L, Son S, Stankovic J, Stoleru R, Wood A (2004) Envirotrack: towards an environmental computing paradigm for distributed sensor networks. In: Proceedings of the 24th international conference on distributed computing systems (ICDCS’04)
[9] Ahn GS, Hong SG, Miluzzo E, Campbell AT, Cuomo F (2006) Funneling-mac: a localized, sink-oriented mac for boosting fidelity in sensor networks. In: Proceedings of the 4th international conference on embedded networked sensor systems (SenSys ’06)
[10] Akyildiz I, Wang X (2005) A survey on wireless mesh networks. IEEE Commun Mag 43(9):S23–S30 · Zbl 1152.68319 · doi:10.1109/MCOM.2005.1509968
[11] Amirijoo M, Hansson J, Son SH (2006) Specification and management of QoS in real-time databases supporting imprecise computations. IEEE Trans Comput 55(3):304–319 · doi:10.1109/TC.2006.45
[12] Bonnet P, Gehrke J, Seshadri P (2001) Towards sensor database systems. In: Proceedings of the second international conference on mobile data management (MDM ’01)
[13] Cook SA, Pachl JK, Pressman IS (2002) The optimal location of replicas in a network using a read-one-write-all policy. Distrib Comput 15(1):57–66 · doi:10.1007/s446-002-8031-5
[14] Deshpande A, Guestrin C, Madden SR, Hellerstein JM, Hong W (2004) Model-driven data acquisition in sensor networks. In: Proceedings of the 30th VLDB conference, Toronto, Canada
[15] Deshpande A, Madden S (2006) Mauvedb: supporting model-based user views in database systems. In: Proceedings of the 2006 ACM SIGMOD international conference on management of data (SIGMOD ’06). ACM, New York, pp 73–84
[16] Desnoyers P, Ganesan D, Shenoy P (2005) TSAR: a two tier sensor storage architecture using interval skip graphs. In: Proceedings of the 3rd international conference on embedded networked sensor systems (SenSys ’05)
[17] Fall K (2003) A delay-tolerant network architecture for challenged Internets. In: Proceedings of the 2003 conference on applications, technologies, architectures, and protocols for computer communications (SIGCOMM ’03), pp 27–34
[18] Fife LD, Gruenwald L (2003) Research issues for data communication in mobile ad-hoc network database systems. SIGMOD Rec 32:42–47 · Zbl 05444098 · doi:10.1145/776985.776991
[19] Gelb A (ed) (1974) Applied optimal estimation. MIT Press, Cambridge
[20] Gnawali O, Jang KY, Paek J, Vieira M, Govindan R, Greenstein B, Joki A, Estrin D, Kohler E (2006) The tenet architecture for tiered sensor networks. In: Proceedings of the 4th international conference on embedded networked sensor systems (SenSys ’06)
[21] Goel S, Imielinski T (2001) Prediction-based monitoring in sensor networks: taking lessons from mpeg. Comput Commun Rev 31:82–98 · Zbl 05745708 · doi:10.1145/1037107.1037117
[22] Gray J, Helland P, O’Neil P, Shasha D (1996) The dangers of replication and a solution. In: SIGMOD ’96
[23] Hellerstein JL, Diao Y, Parekh S, Tilbury DM (2004) Feedback control of computing systems. Wiley/IEEE Press, New York
[24] Intanagonwiwat C, Govindan R, Estrin D, Heidemann J, Silva F (2003) Directed diffusion for wireless sensor networking. IEEE/ACM Trans Netw 11(1):2–16 · Zbl 05458674 · doi:10.1109/TNET.2002.808417
[25] Jain A, Chang EY, Wang YF (2004) Adaptive stream resource management using Kalman filters. In: Proceedings of the 2004 ACM SIGMOD international conference on management of data (SIGMOD ’04). ACM Press, New York, pp 11–22
[26] Jensen CS, Lin D, Ooi BC (2004) Query and update efficient b+-tree based indexing of moving objects. In: Proceedings of the thirtieth international conference on very large data bases (VLDB ’04), vol 30, pp 768–779
[27] Jeung H, Yiu ML, Zhou X, Jensen CS (2010) Path prediction and predictive range querying in road network databases. VLDB J 19:585–602 · Zbl 05828459 · doi:10.1007/s00778-010-0181-y
[28] Jiang H, Jin S, Wang C (2011) Prediction or not? An energy-efficient framework for clustering-based data collection in wireless sensor networks. IEEE Trans Parallel Distrib Syst 22(6):1064–1071 · doi:10.1109/TPDS.2010.174
[29] Jiang X, Chen NY, Hong JI, Wang K, Takayama L, Landay JA (2004) Siren: context-aware computing for firefighting. In: Proceedings of second international conference on pervasive computing
[30] Kang KD, Oh J, Son SH (2007) Chronos: feedback control of a real database system performance. In: RTSS
[31] Kang KD, Son SH, Stankovic JA (2004) Managing deadline miss ratio and sensor data freshness in real-time databases. IEEE Trans Knowl Data Eng 16(10):1200–1216 · Zbl 05110030 · doi:10.1109/TKDE.2004.61
[32] Lazaridis I, Mehrotra S (2003) Capturing sensor-generated time series with quality guarantees. In: Proceedings of 19th international conference on data engineering, 2003, pp 429–440
[33] Le Borgne YA, Santini S, Bontempi G (2007) Adaptive model selection for time series prediction in wireless sensor networks. Signal Process 87:3010–3020 · Zbl 1186.94192 · doi:10.1016/j.sigpro.2007.05.015
[34] Lee EA (2008) Cyber physical systems: design challenges. Tech rep UCB/EECS-2008-8, EECS Department, University of California, Berkeley
[35] Li M, Ganesan D, Shenoy P (2006) Presto: feedback-driven data management in sensor networks. In: Proceedings of the 3rd conference on networked systems design & implementation (NSDI’06)
[36] Lu C, Stankovic JA, Son SH, Tao G (2002) Feedback control real-time scheduling: framework, modeling, and algorithms. Real-Time Syst 23(1–2):85–126 · Zbl 1018.68009 · doi:10.1023/A:1015398403337
[37] Lu C, Wang X, Koutsoukos X (2005) Feedback utilization control in distributed real-time systems with end-to-end tasks. IEEE Trans Parallel Distrib Syst 16(6):550–561 · Zbl 05107494 · doi:10.1109/TPDS.2005.95
[38] Madden SR, Franklin MJ, Hellerstein JM, Hong W (2005) Tinydb: an acquisitional query processing system for sensor networks. ACM Trans Database Syst 30(1):122–173 · Zbl 05457052 · doi:10.1145/1061318.1061322
[39] Mathiason G, Andler SF, Son SH (2008) Virtual full replication for scalable and adaptive real-time communication in wireless-sensor networks. In: Proceedings of the second intl conference on sensor technologies and applications (SENSORCOMM 2008)
[40] Mohapatra P, Gui C, Li J (2004) Group communications in mobile ad hoc networks. Computer 37(2):52–59 · Zbl 05087486 · doi:10.1109/MC.2004.1266296
[41] de Morais Cordeiro C, Gossain H, Agrawal D (2003) Multicast over wireless mobile ad hoc networks: present and future directions. IEEE Netw 17(1):52–59 · doi:10.1109/MNET.2003.1174178
[42] Oh J, Kang KD (2007) An approach for real-time database modeling and performance management. In: Real time and embedded technology and applications symposium, 2007 (RTAS ’07). 13th. IEEE Press, New York, pp 326–336
[43] Olston C, Loo BT, Widom J (2001) Adaptive precision setting for cached approximate values. SIGMOD Rec 30(2):355–366 · doi:10.1145/376284.375710
[44] Padmanabhan P, Gruenwald L, Vallur A, Atiquzzaman M (2008) A survey of data replication techniques for mobile ad hoc network databases. VLDB J 17:1143–1164 · doi:10.1007/s00778-007-0055-0
[45] Pattem S, Krishnamachari B, Govindan R (2008) The impact of spatial correlation on routing with compression in wireless sensor networks. ACM Trans Sens Netw 4:24:1–24:33 · Zbl 05517483 · doi:10.1145/1387663.1387670
[46] Peddi P, DiPippo LC (2002) A replication strategy for distributed real-time object-oriented databases. In: Symposium on object-oriented real-time distributed computing, pp 129–136
[47] Pelanis M, Šaltenis S, Jensen CS (2006) Indexing the past, present, and anticipated future positions of moving objects. ACM Trans Database Syst 31:255–298 · Zbl 05457062 · doi:10.1145/1132863.1132870
[48] Ramamritham K, Son SH, Dipippo LC (2004) Real-time databases and data services. Real-Time Syst 28(2–3):179–215 · Zbl 1094.68559 · doi:10.1023/B:TIME.0000045317.37980.a5
[49] Raniwala A, cker Chiueh T (2005) Architecture and algorithms for an IEEE 802.11-based multi-channel wireless mesh network. In: Proceedings of 24th annual joint conference of the IEEE computer and communications societies (INFOCOM 2005), vol 3. IEEE Press, New York, pp 2223–2234
[50] Ratnasamy S, Karp B, Yin L, Yu F, Estrin D, Govindan R, Shenker S (2002) Ght: a geographic hash table for data-centric storage. In: Proceedings of the 1st ACM international workshop on wireless sensor networks and applications (WSNA ’02)
[51] Santini S, Romer K (2006) An adaptive strategy for quality-based data reduction in wireless sensor networks. In: Proc INSS
[52] Selavo L, Wood A, Cao Q, Sookoor T, Liu H, Srinivasan A, Wu Y, Kang W, Stankovic J, Young D, Porter J (2007) Luster: wireless sensor network for environmental research. In: Proceedings of the 5th international conference on embedded networked sensor systems (SenSys ’07)
[53] Sha K, Shi W, Watkins O (2006) Using wireless sensor networks for fire rescue applications: requirements and challenges. In: IEEE international conference on electro/information technology
[54] Son SH (1988) Replicated data management in distributed database systems. SIGMOD Rec 17(4):62–69 · doi:10.1145/61733.61738
[55] Stankovic JA, Lee I, Mok A, Rajkumar R (2005) Opportunities and obligations for physical computing systems. Computer 38(11):23–31 · Zbl 05090954 · doi:10.1109/MC.2005.386
[56] Tatbul N, Çetintemel U, Zdonik S, Cherniack M, Stonebraker M (2003) Load shedding in a data stream manager. In: Proceedings of the 29th international conference on very large data bases (VLDB ’2003)
[57] Thiagarajan A, Madden S (2008) Querying continuous functions in a database system. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data (SIGMOD ’08). ACM, New York, pp 791–804
[58] Wang X, Jia D, Lu C, Koutsoukos X (2007) DEUCON: Decentralized End-to-End Utilization Control for Distributed Real-Time Systems. IEEE Trans Parallel Distrib Syst 18(7):996–1009 · Zbl 05339632 · doi:10.1109/TPDS.2007.1051
[59] Wei G, Ling Y, Guo B, Xiao B, Vasilakos AV (2011) Prediction-based data aggregation in wireless sensor networks: combining grey model and Kalman filter. Comput Commun 34(6):793–802 · Zbl 05891814 · doi:10.1016/j.comcom.2010.10.003
[60] Wei Y, Son SH, Stankovic JA, Kang KD (2003) Qos management in replicated real time databases. In: Proceedings of the 24th IEEE international real-time systems symposium (RTSS ’03)
[61] Wolfson O, Chamberlain S, Dao S, Jiang L, Mendez G (1998) Cost and imprecision in modeling the position of moving objects. In: Proceedings of the fourteenth international conference on data engineering (ICDE ’98). IEEE Comput Soc, Los Alamitos, Washington, DC, USA, pp 588–596
[62] Zhou Y, Kang KD (2009) Integrating proactive and reactive approaches for robust real-time data services. In: Proceedings of the 30th IEEE international real-time systems symposium (RTSS ’09)
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